Munich Personal RePEc Archive
Determinants of Innovative Activities:
Evidence from Europe and Central Asia Region
Lau, Chi Keung Marco and Yang, Fu Steve and Zhang, Zhe and Leung, Vincent K.K.
Northumbria University, United International College, Eastern Kentucky University
5 January 2013
Online at https://mpra.ub.uni-muenchen.de/52587/
MPRA Paper No. 52587, posted 31 Dec 2013 02:47 UTC
Determinants of Innovative Activities: Evidence from Europe and Central Asia Region
Lau Chi Keung Marco Newcastle Business School
Northumbria University Newcastle, UK
Email: chi.lau@northumbria.ac.uk
Yang Fu Steve
Division of Business and Management United International College
Beijing Normal University-Hong Kong Baptist University Zhuhai, China
Zhe Zhang
Department of Management, Marketing and International Business School of Business
Eastern Kentucky University Kentucky, USA
Leung Vincent K.K.
Division of Business and Management BNU-HKBU United International College
Zhuhai, China
Abstract
Recent studies in the innovation literature reveal that Foreign Direct Investment (FDI) promotes the innovation activities in the recipent country through spillover effects. In this paper we extend the existing literature by incooprating the corruption index in the estimation procedure. Using a cross-country analysis from the Europe and Central Asia (ECA) region , covering 57 countries over the period of 1995-2010, we find no evidence of FDI spillover effect on innovative activity, when corporate corruption is endogenously modelled in the regression. However, corporate corruption and expenditure on education sector are positively related to the number of patents
applications, suggesting anti-corruption programs encourage real innovation activities that promotes economic growth. Our study shed light on the national innovation activities and anti-corruption programs.
Keywords: Foreign direct investment; Corruption; Innovation; Technology transfer JEL classification: O32, O34, O38, F21, D73
1. Introduction
The global economy has yet to shake off the fallout from the crisis of 2008-2009.
Based on estimation of the International Monetary Fund, the gross domestic product of eurozone economy will face 0.1% decline in 2013. A long-term policy is needed to promote sustainable economic growth. Innovation has been widely recognized as a key drive of economic growth and identifying the determinants of innovation is a crucial first step for designing effective policies to enhance economic development and growth. However, despite several studies on this topic (for instance, (Anokhin &
Schulze, 2009)), there is still limited empirical evidence about how countries can promote their innovative capacity, especially in emerging countries where political corruption and corporate corruption prevails.
Corruption is a major obstacle for economic development for developing countries.
Corruption impedes FDI, increases transaction cost and limits entrepreneur’s market (Anokhin & Schulze, 2009). More importantly, corruption delay the permission of licenses and reduce trust of entrepreneur on institution, therefore it impedes the process of innovation. However, some research also shows that corruption can grease the wheel of economic development by speeding the bureaucratic process and jumping policy hurdle (Chen, Liu, & Su, 2013; Wang & You, 2012). With limited and mixed empirical evidence on the influence of corruption on innovation, therefore we need to empirically study what is the impact of corruption on innovation.
The aim of this research is to make a modest contribution towards filling those gaps in existing literature. Our results indicate that research and development expenditures and education expense play a critical role in promoting innovative activity. However, FDI does not have any influence on innovation, and surprisingly, corruption indeed grease the wheel of economic growth and promote innovative capabilities of countries in ECA region.
Obviously, a single empirical research cannot come up with firm conclusions about what factors influence innovative activity among all countries. However, it can shed some new light on national economic policy issues that are also being investigated in other studies on the subject. Our research will help countries in ECA regions to develop powerful policy to promote regional economic growth, such as focusing on education and R&D. Another contribution of the paper is to reveal the effect of corruption on innovative ability.
The rest of the paper is structured as follows. Section 2 presents the theoretical framework of the research. Section 3 presents the data and methodology. Section 4 describes and discusses the empirical results, and section 5 offers some concluding remarks.
2. Theoretical Framework
It has been well established in the literature that innovation promote economic growth, and an increasing number of researchers start to investigate what factors determine the innovative ability of a country in the last few years. One stream of literature focuses on the importance of inputs in the production of knowledge, such as research and development expense, and number of scientists (Acs, Anselin, & Varga, 2002; Furman, Porter, & Stern, 2002). Research and development expenditure by government shows strong positive influence on the number of patents both in developed countries (Furman & Hayes, 2004) and also developing countries (Hu &
Mathews, 2005). Continuous investment on R&D is essential for developing innovative ability of a country. South Korea’s real R&D expenditures in 1999 was 450 more than that in 1978 and at the same time, South Korea demonstrated a dramatic increase in nation’s overall innovation capacity (Furman & Hayes, 2004). Countries with strong commitment on innovation and significant investment on R&D can achieve relatively higher level of innovative capacity. In a general equilibrium model, Fung and Lau (2013) shows that investment price will is a negative function of aggregate quality index; and thus decline over time and subsidy on R&D has growth-enhancing effect.
A narrow focus on the role of science and science related activities cannot fully develop institution’s innovation capacity as new knowledge cannot be produced in vacuum, institutional factors, stated by national innovation system theory, are another strong determinants for innovation ability (Edquist, 1997)1. National innovation system was mainly developed in 1980s and received more attention in 1990s (Lundvall, Johnson, Andersen, & Dalum, 2002). It is used as a framework to analyze science and technology policy in a number of western countries. It helps to understand factors behind international competitiveness and economic development. Initially, the concept of national innovation system was developed mainly in the western countries, e.g. US, UK and now it starts to expand to developing countries but the research in emerging economy is still limited. For developing countries, the institutions play a more important role in economic development compared to developed countries. As in mature market economies, the market solves most problems, so institutional factors have small influence in economic development. Social and economic institutions have shown to demonstrate the variance of innovation ability among countries. For example, differences in economic development (Grande & Peschke, 1999), patent
1 The authors did not explicitly explain institutional factors in details, and we argue in this paper that corporate corruption is one important institutional factor, which is our main contribution to the existing literature of innovation activities.
rights protection (Varsakelis, 2001), and quality of education (Varsakelis, 2006) influences nation’s innovative ability.
FDI has been well recognized as an important factor in national innovation system to promote innovative activities through spill-over effect (Baskaran, 2008). Foreign firms transfer technology to subsidiaries and local firms in host country benefit FDI from a number of ways (Cheung & Lin, 2004). First of all, local firms can imitate the designs of the new developed product of foreign companies by reverse engineering, and build up new innovative product. Secondly, employment and training supplied by foreign firms can enhance the quality of human resource, and those skilled labors will move to other factories in host countries, and therefore knowledge is transferred to other domestic companies. Thirdly, FDI can produce “demonstration effect”. The foreign products in market can stimulate domestic competitor’s innovation to generate ideas for innovative product. Lastly, FDI can promote technological know-how transfer vertically from foreign investing firms to local suppliers through knowledge exchange and training. Then local suppliers can develop innovative products based on vertically spillover knowledge. Besides advantages mentioned above, FDI also creates more jobs and supplies higher wages to workers, therefore many countries develop policy to encourage FDI, however, the effect of FDI on innovation is not conclusive.
The wide techonology gap between developed and developing coutries make the spillover effect subtle (Blomström & Sjöholm, 1999)
Besides FDI, trade is also an important factor which influence innovative activities. Trade facilitates the flow of professional knowledge and diffusion of tecnology (Grossman & Helpman, 1991). It expends the networks of communication of technical knowledge and fosters the colloboration among international partners and reduces duplication of research. Besides, trade can enhance compeititions of both domestic and international market which lead to better product and service, and expand the market size of exporting firms. However, trade also has been shown to increase competition faced by local firm which produce similar products. The effect of positive externatlity from trade could outweitht the competition domestic firms face. Because of fierece compeition from foreign firms, domestic firms may adopt cost minimizing approach, rathen than spending money on innovation, especially for emerging economy, where majorty of firms focus on labor intensive type of job and does not have incentive to develop innovative product. Therefore, trade may decrease the level of innovative activities. Cheung and Lin (2004) stresses that firms with larger export–output shares cannot signigicantly benefit from intrnational trade because the foreign firms coming to China only want to utilize China’s cheap labor, and hence the technologies they bring in are mostly labor intensive and the spillover
effects on domestic innovation is not strong. In the paper of Cheung and Lin (2004) they use a standard model that implicitly assumes Chinese economy as a formal economy with free market economy. Therefore in their empirical modeling strategy they ignore the most crucial informal institutional feature of corporate corruption. And this ignorance makes the estimates of FDI invalid because of omitted variable problem (i.e. corporate corruption was omitted). Apart from FDI some other channels can also transfer technology and skills from one country to another: in the study of Bilgin, Lau and Demir (2012)
The outcome of innovation activities is difficult to measure, however the number of patent is a good proxy to reflect innovation activities in each countries(Acs et al., 2002). The number of patents application has increased continuously in the last decades, which demonstrates the importance of patents in knowledge based economy.
Patent is a good method to track the knowledge among firms, universities and countries. In this paper, we focus on the definition on the OECD manual (OECD, 2005), and broadly innovations can be categorized into four different types: Product Innovations: Introduction of a good or service that is new or significantly improved with respect to its characteristics and intended uses. Process Innovations:
Implementation of a new or significantly improved production or delivery method.
This includes significant changes in techniques, equipment and/or software. The process innovations can be intended to decrease unit costs of production or delivery.
Marketing Innovations: Implementation of a new marketing method involving significant changes in product placement, promotion etc. Examples of marketing innovations include introduction or obtaining new product licensing. Organizational Innovations: Implementation of new organizational method in firm’s business practices, workplace organization and external relations.
Another important factor which influences innovative activities is the efficiency of political institutions (Varsakelis, 2006). In national innovation system, dynamic networks of policies and institutions influence knowledge transfer among different countries and also within each country’s domestic industries. In order to absorb knowledge from foreign countries, an institution needs to implement policies that facilitate domestic firms to use and diffuse these technologies within domestic industry. Previous research has shown that the intellectual property protection framework influences a country’s innovation ability (Varsakelis, 2001). A country’s ability to enact a law bases on the quality of institutional agencies such as political stability and judiciary system. Efficient judiciary system can provide better protection on patents and therefore, entrepreneurs have higher incentive to innovate. However, countries with high corruption and low enforcement of law will affect diffusion of
knowledge and impede innovation. Research shows that corruption and abuse of public power undermines the foundations of institutional trust and consequently hinder the innovative ability of entrepreneurs (Anokhin & Schulze, 2009). In the literature, corruption has been widely used as a proxy for the efficiency of political institutions (Mauro, 1995; Varsakelis, 2006).
Despite the fact that a growing number of studies demonstrate importance of national innovation system in developed countries, limited research has been conducted to investigate national innovation system approach specific to developing countries. US, UK and Scandinavia countries are among the first to investigate national innovation system in order to understand factors behind economic development and growth. Focus has started to move to developing countries which show strong economic growth, like Asian countries. Also developing countries need effective policy guidelines to promote domestic innovative activities. Therefore in this study, we choose European and Central Asia (ECA) regions to study country specific effect of national innovation system. One reason for choosing ECA region is the fact that spatial proximity is an important force which facilitate flow of information and knowledge, as documented in the literature on innovative activity (de Dominicis, Florax, & de Groot, 2012; Jaffe, 1989). It has been well accepted that geographic proximity aid learning processes through mechanisms of knowledge spillovers, especially sticky knowledge. Tacit knowledge is un-codified and can only be acquired through the process of social interaction. The chance that tacit knowledge is transferred from one region to another region decreased when the geographic distance increase. Therefore, the closer a country to other innovative countries, the more chance of knowledge transfer between two countries and the more likely recipient countries exhibit a high capacity to introduce new products or processes.
One of major obstacles currently faced by ECA countries is corruption, which is common among emerging countries. Substantial research has demonstrated detrimental effect of corruption on economic development. It is well recognized that corruption increases agency costs, limits firm’s revenues, undermine institutional trust (Anokhin & Schulze, 2009; Mauro, 1995). However, due to the complex relationships and associated data limitations for conducting studies2, the direct impact of corruption on innovative activity is still not clear based on current empirical studies. Especially,
2 Only few studies have been related to this issue, and these provide mixed evidence. For example, Anokhin et al. (2008) find that countries with higher control of corruption (derived from World Bank’s Worldwide Governance Indicators) are associated with higher number of patents application.
Mahagaonkar (2008) find that corruption has a positive effect on marketing innovation and negative effect on product innovation and organization innovation.
entrepreneurs in developing countries often encounter corruption problems, and resource was allocated based on the relationship with government, usually through bribing. Therefore, it is even more important to investigate whether and to what extent corruption adversely affect the innovation activity in emerging countries. To date, this research topic has rarely been tested in empirical studies, and our study will fill some gaps in the current literature.
High-tech firms play a crucial role in the transformation process from a developing economy to a developed economy. However, corruption undermines the foundations of institutional trust that are needed for the development of trade and innovative activity (Anokhin & Schulze, 2009; Varsakelis, 2006). Corruption taxs on trade and impedes trade between deveoloped and developing countries (Dutt & Traca, 2010). FDI investors has higher chance to terminate contract with local partners of international joint venture in face of government corruption in emerging economy (Meschi, 2009). When corruption is present, FDI investors are reluctant to transfer advanced technology to corrupted countries as they face greatly increased risk. And in the absence of impersonal enforcement of law, it becomes risky to rely on legal contracts. Hellman found that corrupt nations tend to attract FDI from other corrupt nations, and less corrupt nations tend to attract FDI from less corrupt nations, where institutional trust is stronger and company face smaller risk of technology stolen (Hellman and Kaufmann, 2004). Similar empirical result shows that corrupted countries received less FDI from OECD countries and received more FDI from other corrupted countries. It suggests that investors who have experience in bribery at home are more willing to invest in countries with corruption practice (Cuervo-Cazurra, 2006). Companies from less corrupt countries tend to enter using direct entry model in less corrupt countries rather than joint venture, which is commonly used in corrupt economies (Smarzynska & Wei, 2000). Therefore, corrupt nations are less likely to receive investment from high-tech companies that employ sophisticated technologies.
Because ECA countries’ judiciary system is not transparent, e.g. Georgia before 2003 reform (worldbank 2013), foreign investors are reluctant to transfer advanced knowledge to this region, which may cause low quality innovation and therefore slow economic growth.
The Corruption Perception Index (CPI) is calculated by Transparency International and has been widely used as a measure for corruption (Varsakelis, 2006).
CPI is based on survey of business people and industry expert over hundred countries.
It measures those persons’ perception about the level of corruption in particular country. However, this subjective measure may not truly reflect the local situation.
Instead of using perception, we adopt a real measurement which is collected by World Bank. We use firm’s informal payments to government as a measurement of
corruption. This variable measures the percentage of firms that pay informal payments or gift to the public officials in a particular country.
Previous studies on national innovation system have analyzed the impact of corruption and FDI on innovation in separate but parallel research paths. In this study, we explore how the corruption and FDI together affect the innovation activities in ECA countries. This approach distinguishes our study from all previous empirical research which only investigates on each factor. To our knowledge, this paper is the first one to investigate the impact of FDI and corruption on innovative activities in ECA regions.
Education plays a critical role in developing innovative capability of a country.
Furman et al. (2002) demonstrated difference in education resources can explain the cross-country variation of innovation ability also Varsakelis (2006) shows the quality of education can explain the cross-country variation of innovation productivity.
Formal education can improve student’s ability to learn and move the society into a learning economy. The learning capacity of individuals, firms and countries are most important elements in a national innovation systems (Lundvall et al., 2002). Quick learner can adapt to the changes of environment and technology and create new innovative product based on the evolving knowledge. Rapid learner can quickly understand explicit knowledge which is codified, and more importantly, they can learn tacit knowledge from other professionals, organizations and institutions.
Managers captured domain expertise through formal education and educated manger can make decision to sustain the development of company and promote innovative activities (Holcomb, Holmes Jr, & Connelly, 2009). Education of staffs in a company influences the absorptive ability of firms and further affects firm’s innovative performance (Lund Vinding, 2006).
3. Data and methodology
3.1 The Sample
The World Bank collection of development indicators covers 256 countries, with seven regions over the world. Judged from the demographic distribution of the seven regions, we decided to focus on Europe and Central Asia (ECA) region because of its abundant data available that enable us to form a more balanced panel data, as compared to other regions. More importantly ECA region represent an interesting study on the positve spillover effect of FDI on product innovation (measured as the number of patents application in the home country) due to its local proximity to each
other.3
Table 1. 57 Countries included in the analysis (1995–2010)
Albania Faeroe Islands Latvia Serbia
Andorra Finland Liechtenstein Slovak Republic
Armenia France Lithuania Slovenia
Austria Georgia Luxembourg Spain
Azerbaijan Germany Macedonia, FYR Sweden
Belarus Greece Moldova Switzerland
Belgium Greenland Monaco Tajikistan
Bosnia and Herzegovina Hungary Montenegro Turkey
Bulgaria Iceland Netherlands Turkmenistan
Channel Islands Ireland Norway Ukraine
Croatia Isle of Man Poland United Kingdom
Cyprus Italy Portugal Uzbekistan
Czech Republic Kazakhstan Romania
Denmark Kosovo Russian Federation
Estonia Kyrgyz Republic San Marino
Our study is based on data from ECA and European countries for the period of 1995-2010. Number of patent application was used as a measure of innovative activity.
ECA countries encountered series of transition process from late 1980s to early 1990s.
Since then, inventive activity has shown a clear increasing trend and this generally positive trend has been maintained up to the most recent years for which data are available (Figure 2.1). Especially, Russian and Poland shows stronger increase in the number of patent application in this period of time.
3 The diverse yet highly interdependent economies of Europe and Central Asia are a natural experiment in seeing how the emerging economies can learn from the developed European countries. In our sample, advanced European countries including: Austria, Belgium, Germany, Denmark, Spain, France, Finland, Iceland, Norway, Portugal, Greece, Italy, Ireland, Luxembourg, the Netherlands, Portugal, Switzerland, Sweden and the United Kingdom.
Figure 1. Number of patent applications (1995-2010)
Figure 2.1 shows a long-run perspective on ECA patenting by tracking all patent applied to State Patent Office (data from World Bank indicator). Selected ECA includes: Russia, Hungary, Poland, Czech Republic, Slovenia and Ukraine. We can see from the figures that ECA countries did catches up with Germany, while the number of patents in the UK is decreasing over time. An interesting question that which emerging countries copy innovation from European countries is usually lacked in the literature. And the number of patents applications in the well developed European countries may impose positive externality on ECA countries. In order to answer an interesting question of which emerging countries receives positive external benefits from which group of EU countries we therefore conduct granger causality test. The variable of interest is the number of patent applications. Table 2 shows the empirical findings after examining all countries in our sample, detailed statistics are available upon request. The result shows that Hungary, Czech Republic, Ukraine, Slovenia, and Estonia all benefits from the innovation activities that led by Spain.
Table 2 Granger Causality Test
Spain Germany Belgium
Hungary Ukraine Turkey
0 10000 20000 30000 40000 50000 60000
1995199619971998199920002001200220032004200520062007200820092010 German UK Selected ECA
Czech Republic Rusia Czech Republic Ukraine Hungary
Slovenia
Estonia
Note: Evidences are based on 5% significance level
Figure 2 Maps of Granger Causality Test Results
Table 3 Summary statistics
Variable Mean Std. Dev. Min Max
Ln (Patent) 5.60 1.82 0.69 10.27
Ln (R&D Exp/GDP) 0.61 0.38 0.02 1.86 Ln (Number of Researchers) 7.12 0.74 4.12 8.24
Ln (Trade) 99.31 31.83 36.55 199.68
Ln (Education Expenditure) 22.95 1.70 19.22 27.08
Ln (FDI Inflow) 1.36 0.95 -1.77 3.95
Corruption (% of firms) 36.54 18.92 3.70 77.42
Source: World Bank Indicator
Tables 3 above shows the variables we used for regression analysis to examine the determinants of the number of patents applications, one striking figure is that 36%
of firms on average reports of having been committed bribery to government officials for getting things done. For some countries this number reaches 60-77 percent, which indicates that the very important feature of informal institution in emerging
economies is corporate corruption. And this variable of corporate corruption once ignored in the empirical analysis, it will causes omitted variable biase.
3.2 Methodology
We now turn to the empirical parts; we focus on panel regression analysis to examine the detrminanats of product innovations. Following Cheung and Lin (2004) the innovation production function in its empirical form can be represented as:
it t i t i t
i t
i k t
i X FDI Corrup v v
Patent, 0 , , , (1)
where Patenti,t is the number of patent application to quantify the innovation level for country i at timet; the larger the number of patent application, the higher the innovation level. Xi,tis the matrix for the country’s inputs into the R&D activities.
vi is the fixed effect for province i, vtis the time dummy, and itis the idiosyncratic disturbance. The idiosyncratic disturbances are assumed to be uncorrelated across the countries. Innovation is a knowledge creation process, the more the inputs the hihger the chance of success. Therefore the measure of inputs to R&D activities (Xi,t) includes:
NUMBER OF R&D RESEARCHERS
The variable measures the number of personnel (experts) in the R&D sector, it proxy the labour input to the R&D activity. We expect positive association bewteen this control variable and the number of patents applications.
R&D EXPENDITURE PER CAPITA GDP
This variabe measures the R&D intensity, it proxy not only the quantity of resources deveotd into the R&D activities but also the quality of capital and human resources into R&D processes. Following Cheung and Ping (2004) we use the amount of expenditures spend on R&D sectors to poxy the resources, such as technicians, equipments and scientists that used to create new knowledge. We again expect positive association between this variable and innovation.
EXPENDITURE ON EDUCATION
Since general eduxation is the foundation of any innovation activities, therefore we
use exenditures on education , which was genrally ignored in the literature to proxy the positive externality effect of genral education as a public goods. We expect postive relatiohsop between this variable and the inoovation outcome.
OPENNESS
Here we also include the variable “OPENESS” , defined as the summation of imports and exports, to test if domestic firms can benenfit in domestic innovation from participating in the ovrseas markt. Hoever we expect this effect is week and even negative. Cheung and Ping (2004) finds that FDI firms with larger export–output shares cannot signigicantly benefit from intrnational trade because the FDI firms come to China only to utilize its cheap labor, and hence the technologies they bring in are mostly labor intensive and the spillover effects on domestic innovation is not strong. In our study FDI firms’s export to GDP ratio is generally not avaible for ECA coutnries. Therefore we expect even a negative effect assocaited with innovation and openss becuase most of these emerging markets only perform labor intensive process and lack of incentive to do its own nnovation if their economy is too much relie on exporting labor-intensive peoducts. Moreover, trade can pose negative impact on innovation through competition4.
Turning to FDI, as we discueed extensively spillover effects of FDI may have positive influence on the number of domestic patent application. However the uncertainty of this hypothsis come from two sources. First this assumed association all depends on the form of ownership structure of the enterprises. Obviosuly, foreign joint ventures and cooperative businesses are able to gnerate positve spillover effect than exclusively foreign-owned enterprises for instance. More importantly corruption may trigger FDI and hence the effect of FDI on innovation may be biased when the vaiable of corruption is ommited. Therefore in our empirical regression model we include the variable of corporate corruption (Corrupi,t), which measures the percentage of firms that pay informal payment or gift to the public officials in a particular counry. As we emphasized in the literature, there is lack of research on the association of corporate corruption and innovation activities, espcially for the emerging markets, where resource allocation is often shaped by political connection.
As a result, it is important to know whether and to what extent corruption is adversely affect the innovation activity of in emerging countries. To date, the impacts of corruption on innovation have rarely been empirically tested. In this paper, we aim to
4 As noted by Onodera (2008), an increase in competition can have both positive and negative effects on innovation depending on levels of existing competition, nature of the industry, and existing levels of technology.
fill some gaps in the existing literature by focusing on whether corruption can adversely affect the innovation ability of firms in ECA region. In contrast to emerging markets, anti-corruption programs and regulations are well-established in the developed countries alike Western Euoropen countries like Germeny and France.
Unfortunately the corporate corruption data is not avaible for developed countries, otherwise it would be interesting to conduct a compartive study to compare the impact of corporate corruption for emerging and developed markets.
4. Empirical Result
Several estimation methods are considered in this study. Column 1, 3, and 5 of table 4 shows the baseline random effect estimation5 of determinants of innovation activities countries from ECA region. Several empirical findings are apparent.
Column 1 shows the baseline modeling of the determinants (control variables) of product innovation. In general we observe positive correlation between R&D personnel and innovation activities, even though only model 3 is statistically significant at 10% level, while the coefficient in model 1 is marginally significant. For all random effects models, the expenditure on R&D intensity has positive impact on product innovation, indicating that the success rate of innovation becomes higher when the country devotes larger amount of resources to the sector, and the results are expected and consistent with the existing literature. However, there is no guarantee of having more innovations even when more human capital, as measured by the number of researchers is working in the sector. The estimate for public expenditure on education is positive and statistically significant at 1% level, supporting the hypothesis that the higher the investment of a society in general education, the more efficient the innovation sector will become, as positive externality exists. The negative impact of openness on innovation activities is observed, and this finding implies that the negative effect on innovation raised from increase in competition outweighs it positive contribution to innovation activities. As suggested by Onodera (2008), the mixed effects of openness on innovation depending on levels of existing competition, nature of the industry, and existing levels of technology.
Table 4: Determinants of Innovation
(1) (2) (3) (4) (5) (6)
5 We use heteroskedasticity-robust standard errors clustered at the country level, such that the computed t-values have been taken into account of the within-country but between-year correlation.
The Hausman specification test indicates that the random effect model should be used. Results are not shown here to save space.
RE SGMM RE SGMM RE SGMM
Number of R&D Researchers
0.466 -0.0707 0.460* -0.378 0.191 0.548
[1.64] [-0.14] [1.69] [-1.02] [0.59] [0.42]
R&D
EXPENDITURE
0.422** 0.105 0.469*** 0.325 1.080*** -0.380
[2.27] [0.30] [2.71] [0.78] [2.67] [-0.10]
OPENESS -0.00996*** -0.0214** -0.0105*** -0.0207*** -0.00985 0.00924
[-4.00] [-2.21] [-3.88] [-2.73] [-1.57] [0.58]
EXPENDITURE ON
EDUCATION
0.384*** 0.824*** 0.365*** 0.803*** 0.738*** 1.138***
[3.85] [5.85] [3.65] [6.29] [7.14] [3.06]
FDI INFLOW 0.0583* 0.0889 0.0891 -0.254
[1.94] [1.22] [0.88] [-0.30]
CORRUPTION OF FIRMS
0.0132* 0.0438***
[1.92] [2.94]
Constant 0.394 -9.667*** 1.068 -7.562* -6.494*** -20.05
[0.21] [-3.75] [0.61] [-1.86] [-3.13] [-1.48]
Observations 401 380 392 371 51 51
Adjusted R2 0.570 0.505 0.815
AR(2) p-value
0.178 0.138 0.143
Hansen p-value
0.418 0.639 0.539
t statistics in brackets; * p < 0.10, ** p < 0.05, *** p < 0.01
We now turn our focus to the spillover effect of FDI on innovation. It seems that the positve spillover effect of FDI exists; however, this effect disappears once we include the variable of corporate corruption. Conlumn 5 reveals the fact that the
higher the percentage of firms that pay informal payment or gift to the public officials in a particular counry, the higher is the number of patents applications. This finding is not surprising since resource allocation is often shaped by political connection, and firm’s innovation is no exception. Therefore, we conclude that innovation activities in the ECA region cannot truly reflect the innovation outcomes because the numbers of patents applications is connected to corruption activities. Corruption can adversely affect the innovation ability of firms in ECA region. The job of patent exminers is to examine whether the claimed innvenion application should be granted the patent. The quality of patents applications are adversely affected because the salary of the patent examiners are not high in emerging countries. Also, the growth of corruption , nepotim, non-transparent practices, and non-accountability of admistractive officers who are in power also cause inefficieny of the patent office in assessing and approving patent applications. Therefore it is important to establish an effective anti-corruption compliance program in order to prevent and detect patents applications which are not up to standard. We can conclude that R&D intensity is the most important determinat of innovaton actvity, followed by expenditure on education.
For robustness check of findings for the estimates reported in Table 4 using random effects method (column 2,4, and 6 in table 4), we adopt Windmeijer (2005) general method of moment (SGMM) system panel data estimator, with the two-step finite-sample correction, to deal with the handle possible endogenity of the independent variables, raised possibly from simultaneity bias, reverse causality and omitted variables. In our case the more open is the international trade (openness), it may stimulate more domestic innovation in face of intense competition. However the reverse causality can happen; whereas more innovations may create more trade opportunities. The same may happen for the relationship between corporate corruption and innovation. It may be in the direction that more patent applications attract more frequent bribery activity. And the use of GMM estimation can overcome the endogeneity bias, and control for fixed effects and time effects, and multiple endogenous variables. In our paper we use system GMM because the conventional dynamic GMM coefficients will be biased for small samples if the series are near unit root processes, and the instruments variables are weak.
In order to check for the consistency of the GMM estimator, we use Hansen test to detect overall validity of the instruments, under the null hypothesis that the residuals and instrumental variables are not correlated. In our model we also perform a second order autocorrelation test for the residuals, to test whether second order serial correlation exists in the estimation models. As we can see the presence of the lagged dependent variable gives rise to autocorrelation, with correlation of 0.991 between
patents applications and its first lag. We used the “xtabond2” Stata routine developed by Roodman (2005). The explanatory power of the random effects model is quite satisfactory, with R2 of 0.815 after taking into the effect of corporate corruption. (See column 5, table 4) However, the results for GMM estimates are also provided for robustness checking because of the potential endogeneity problem. This study uses a two-step estimator, which is asymptotically efficient and robust to any pattern of cross-correlation and heteroskedasticity (Roodman, 2006). Even though there seems no prior knowledge regarding exogeneity of regressors we use the number of telphone line as the IV. The corrleation coefficient between numbers of telephone lines and corruption is -0.573 while the correlation coefficient between numbers of telephone lines and patents applications is 0.222. The result of the SGMM estimation is shown in the column 2, 4 and 6 of Table 4. The validiy if IVs are checked by using Autocorrelation AR(2) test, and Hansen test. The instruments used in the model are valid as we can see from the results of the above two tests. When we compared the results of SGMM (column 6) with the FE results (column 5) we find that expenditure on R&D activity and numbers of personnel are not significant for the number of patents applications. Interstingly the coefficient and its statistical significance increases in the SGMM estimation, and this result futher concide with our argument that the numbr of patents applications are not an accuartate indicator of innovation activities. Instead higher number of patents applications in emerging economy is associated with bribery. Our empirical results regarding the relationship between patents applications and corporate corruption is robust for a variety of models.
5. Conclusion
As the world becomes flat, the interests in entering global markets have surged phenomenally. Since markets differ significantly in their business environments, firms are cautious in choosing which market to enter and develope FDI and innovation activities. In this study, we attempt to provide a deeper understanding of how countries differ with respect to their innovations. Specifically, we investigated the effects of FDI, corruption and educational expenditure on innovation in emerging economies. In previous studies the feature of corporate corruption as an crucial informal institution has not been modelled. Therefore, in our study, using World Bank’s archival dataset that contains 57 countries, we found that educational expenditure, and corporate bribery are positively related to innovations. This finding implying FDI in emerging economy, alike China has been overestimeted, simply because the features of an informal economy has not been taken into account in the regression analysis. The interesting finding of positive effect of bribery on patents
applications posts caution on the fact that corruption hinders the real innovation activities, and hence economic growth and productivity.
Reference
Acs, Z. J., Anselin, L., & Varga, A. (2002). Patents and innovation counts as measures of regional production of new knowledge. Research Policy, 31(7), 1069‐1085.
doi: http://dx.doi.org/10.1016/S0048‐7333(01)00184‐6
Anokhin, S., & Schulze, W. S. (2009). Entrepreneurship, innovation, and corruption.
Journal of Business Venturing, 24(5), 465‐476. doi:
http://dx.doi.org/10.1016/j.jbusvent.2008.06.001
Baskaran, A. a. M., Mammo. . (2008). The impact of the national innovation systems on the flow and benefits of foreign direct investment to national economics.
Working Paper. DIIPER & Department of History, International and Social Studies.
Bilgin, M. H., Lau, C. K. M., Demir, E. (2012). Technology Transfer, Finance Channels, And Sme Performance: New Evidence From Developing Countries. The Singapore Economic Review, 57(03), 12500‐12520.
Blomström, M., & Sjöholm, F. (1999). Technology transfer and spillovers: Does local participation with multinationals matter? European Economic Review, 43(4–6), 915‐923. doi: http://dx.doi.org/10.1016/S0014‐2921(98)00104‐4
Chen, Y., Liu, M., & Su, J. (2013). Greasing the wheels of bank lending: Evidence from private firms in China. Journal of Banking & Finance(0). doi:
http://dx.doi.org/10.1016/j.jbankfin.2013.02.002
Cheung, K.‐y., & Lin, P. (2004). Spillover effects of FDI on innovation in China:
Evidence from the provincial data. China Economic Review, 15(1), 25‐44. doi:
http://dx.doi.org/10.1016/S1043‐951X(03)00027‐0
Cuervo‐Cazurra, A. (2006). Who cares about corruption? Journal of International Business Studies, 37(6), 807‐822. doi: 10.1057/palgrave.jibs.8400223
de Dominicis, L., Florax, R. J. G. M., & de Groot, H. L. F. (2012). Regional clusters of innovative activity in Europe: are social capital and geographical proximity key determinants? Applied Economics, 45(17), 2325‐2335. doi:
10.1080/00036846.2012.663474
Dutt, P., & Traca, D. (2010). Corruption and Bilateral Trade Flows: Extortion or Evasion?
Review of Economics and Statistics, 92(4), 843‐860. doi:
10.1162/REST_a_00034
Edquist, C. (1997). Systems of Innovation: Technologies, Institutions and Organizations. Pinter, London.
Fung, K.W. T., C.K. M. Lau., 2013. Financial Development, Economic Growth and R&D Cyclical Movements. Journal of Applied Economics and Business Research, 3(3), 133‐145.
Furman, J. L., & Hayes, R. (2004). Catching up or standing still?: National innovative productivity among ‘follower’ countries, 1978–1999. Research Policy, 33(9), 1329‐1354. doi: http://dx.doi.org/10.1016/j.respol.2004.09.006
Furman, J. L., Porter, M. E., & Stern, S. (2002). The determinants of national innovative capacity. Research Policy, 31(6), 899‐933. doi:
http://dx.doi.org/10.1016/S0048‐7333(01)00152‐4
Grande, E., & Peschke, A. (1999). Transnational cooperation and policy networks in European science policy‐making. Research Policy, 28(1), 43‐61. doi:
http://dx.doi.org/10.1016/S0048‐7333(98)00099‐7
Grossman, G., & Helpman, E. (1991). Innovation and Growth in the Global Economy (Vol. null).
Hellman, J., Kaufmann, D.,. The inequality of influence. In: Kornai, J., Rose‐Ackerman, S. (Eds.), Trust in Transition. Palgrave‐Macmillan, New York.
Holcomb, T. R., Holmes Jr, R. M., & Connelly, B. L. (2009). Making the most of what you have: managerial ability as a source of resource value creation. Strategic Management Journal, 30(5), 457‐485. doi: 10.1002/smj.747
Hu, M.‐C., & Mathews, J. A. (2005). National innovative capacity in East Asia.
Research Policy, 34(9), 1322‐1349. doi:
http://dx.doi.org/10.1016/j.respol.2005.04.009
Jaffe, A. B. (1989). Characterizing the “technological position” of firms, with application to quantifying technological opportunity and research spillovers.
Research Policy, 18(2), 87‐97. doi:
http://dx.doi.org/10.1016/0048‐7333(89)90007‐3
Lund Vinding, A. (2006). Absorptive capacity and innovative performance: A human capital approach. Economics of Innovation and New Technology, 15(4‐5), 507‐517. doi: 10.1080/10438590500513057
Lundvall, B.‐Å., Johnson, B., Andersen, E. S., & Dalum, B. (2002). National systems of production, innovation and competence building. Research Policy, 31(2),
213‐231. doi: http://dx.doi.org/10.1016/S0048‐7333(01)00137‐8
Mauro, P. (1995). Corruption and Growth. The Quarterly Journal of Economics, 110(3), 681‐712. doi: 10.2307/2946696
Meschi, P.‐X. (2009). Government corruption and foreign stakes in international joint ventures in emerging economies. Asia Pacific Journal of Management, 26(2), 241‐261. doi: 10.1007/s10490‐007‐9067‐y
OECD. (2005). Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data (3rd Ed.)
OECD Publishing.
Smarzynska, B. K., & Wei, S.‐J. (2000). Corruption and Composition of Foreign Direct Investment: Firm‐Level Evidence
National Bureau of Economic Research Working Paper Series, No. 7969(2000).
Varsakelis, N. C. (2001). The impact of patent protection, economy openness and national culture on R&D investment: a cross‐country empirical investigation. Research Policy, 30(7), 1059‐1068. doi:
http://dx.doi.org/10.1016/S0048‐7333(00)00130‐X
Varsakelis, N. C. (2006). Education, political institutions and innovative activity: A cross‐country empirical investigation. Research Policy, 35(7), 1083‐1090. doi:
http://dx.doi.org/10.1016/j.respol.2006.06.002
Wang, Y., & You, J. (2012). Corruption and firm growth: Evidence from China. China Economic Review, 23(2), 415‐433. doi:
http://dx.doi.org/10.1016/j.chieco.2012.03.003
World Bank. 2012. Fighting corruption in public services : chronicling Georgia's reforms. Directions in development: public sector governance. Washington D.C. ‐ The Worldbank.
http://documents.worldbank.org/curated/en/2012/01/15647088/fighting‐corruptio n‐public‐services‐chronicling‐georgias‐reforms
